Background: Originating from the cold and arid regions of northwestern China, Kazakh sheep are dual-purpose breeds optimized for both meat and fat production. In contrast, Texel sheep are internationally recognized for their high-quality meat and exceptional flavor. Previous studies have indicated that the hybrids of Texel and Kazakh sheep exhibit significant quality advantages. Additionally, body weight is a crucial indicator of sheep production performance, directly correlating with meat yield and economic returns. Objective: This study aims to identify genetic variations and related genes associated with the body weight traits of hybrid lambs, thereby revealing their genetic mechanisms. Methods: This study genotyped hybrid lambs using a 50K chip and performed rigorous quality control on both genotypic and phenotypic data. The traits examined include body weight traits of lambs at various stages such as birth, pre-weaning, and post-weaning. Various genome-wide association study (GWAS) models were utilized to analyze the association between lamb body weight traits and genetic markers. The study then employed an Ensemble-like GWAS (E-GWAS) strategy to integrate these models, achieving a stable list of SNPs, rather than a mere aggregation. Multiple annotation databases were consulted to further investigate the mechanisms by which genetic markers affect body weight traits. All study results were validated through an extensive literature review. Results: Analyses with multiple statistical models revealed that 48 SNPs were significantly associated with body weight traits. The annotation process identified 24 related genes (including 4 unknown genes) and 9 quantitative trait loci (QTLs). Additionally, 6 Gene Ontology (GO) terms and 22 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined. Conclusions: This study identified key genes and pathways in the body weight traits of hybrids between Texel and Kazakh sheep, enhancing our understanding of their genetic mechanisms.

To explore the complex spatial pattern between the incidence of hand, foot, and mouth disease (HFMD) and meteorological factors [average temperature (AT), average relative humidity (ARH), average air pressure (AP), average wind speed (AW)], this paper constructed a Spatial Clustering coefficient (SCC) regression model to detect spatial clustering patterns of each regression coefficients in different seasons. The results revealed that compared with geographically weighted regression (GWR), the coefficients estimated by SCC method were more smooth with clearly identified spatial and improved edge effects. Therefore, interesting spatial patterns were easy to identify in the SCC estimated coefficients. And then, the SCC method had better estimation accuracy in estimating the relationship between potential meteorological factors and HFMD cases. Meteorological factors had different significance in their effect on HFMD incidence depending on the season. Specifically, the influence of AT on HFMD was negatively correlated in summer and winter, especially in the Altay region, Bayingoleng Mongolian Autonomous Prefecture, Turpan region and Hami region. Second, AW had positive effects with HFMD in summer, but the AW played a negative role in the whole Xinjiang in winter. In Tianshan district, Shayibake district, Shuimogou district, etc. in summer, ARH showed a strong negative correlation, but in Alar city it had a high positive correlation, however, in winter ARH showed a high negative correlation in Altay regions, Aksu region and other places had negative effects, and it showed a strong positive correlation in Shayibak district. Finally, AP had a strong positive correlation with HFMD in summer in Shaybak district, but in winter, AP showed a strong negative correlation in Altay district and Buxel Mongolia Autonomous county. In summary, Xinjiang should adapt measures to local conditions, and formulate appropriate HFMD prevention strategies according to the characteristics of different regions, time, and meteorological factors.

Medical data are often missing during epidemiological surveys and clinical trials. In this paper, we propose the MCMCINLA estimation method to account for missing data. We introduce a new latent class into the spatial lag model (SLM) and use a conditional autoregressive specification (CAR) spatial model-based approach to impute missing values, making the model fit into the integrated nested Laplace approximation (INLA) framework. Combining the advantages of both the Markov chain Monte Carlo (MCMC) and INLA frameworks, the MCMCINLA algorithm is used to implement imputation of the missing data and fit the model to derive estimates of the parameters from the posterior margins. Finally, the economic data and the hemorrhagic fever with renal syndrome (HFRS) disease data of mainland China from 2016–2018 are used as examples to explore the development of public health in China in the post-epidemic era. The results show that compared with expectation maximization (EM) and full information maximum likelihood estimation (FIML), the predicted values of the missing data obtained using our method are closer to the true values, and the spatial distribution of HFRS in China can be inferred from the imputation results with a southern-heavy and northern-light distribution. It can provide some references for the development of public health in China in the post-epidemic era.

The Multiscale Geographically and Temporally Weighted Regression model overcomes the limitation of estimating spatiotemporal variation characteristics of regression coefficients for different variables under a single scale, making it a powerful tool for exploring the spatiotemporal scale characteristics of regression relationships. Currently, the most widely used estimation method for multiscale spatiotemporal geographically weighted models is the backfitting-based iterative approach. However, the iterative process of this method leads to a substantial computational burden and the accumulation of errors during iteration. This paper proposes a non-iterative estimation method for the MGTWR model, combining local linear fitting and two-step weighted least squares estimation techniques. Initially, a reduced bandwidth is used to fit a local linear GTWR model to obtain the initial estimates. Then, for each covariate, the optimal bandwidth and regression coefficients are estimated by substituting the initial estimates into a localized least squares problem. Simulation experiments are conducted to evaluate the performance of the proposed non-iterative method compared to traditional methods and the backfitting-based approach in terms of coefficient estimation accuracy and computational efficiency. The results demonstrate that the non-iterative estimation method for MGTWR significantly enhances computational efficiency while effectively capturing the scale effects of spatiotemporal variation in the regression coefficient functions for each predictor.

Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of different subjects and the spatial random effects to further analysis the influence of spatial effect. The model is verified to be compatible with the integrated nested Laplace approximation (INLA) framework and is fitted using INLA and stochastic partial differential equation (SPDE). The posterior marginal distribution of parameters is estimated with high precision. Additionally, a practical application of the model is presented using tuberculosis (TB) incidence data in China from 2015 to 2019. The results show that the fitting accuracy of our model is higher than the general Bayesian spatial model using INLA-SPDE.